class_docstr: Computes Binary Cross Entropy (BCE) loss. The BCELoss class expects output from Sigmoid function.
docstr: Calculate binary cross entropy loss based on probs, labels and signal_lengths variables.
  
  Args:
      probs (torch.tensor)
          Predicted probability value which ranges from 0 to 1. Sigmoid output is expected.
      labels (torch.tensor)
          Groundtruth label for the predicted samples.
      signal_lengths (torch.tensor):
          The actual length of the sequence without zero-padding.
  
  Returns:
      loss (NeuralType)
          Binary cross entropy loss value.
docstr_tok: ["Calculate","binary","cross","entropy","loss","based","on","probs",",","labels","and","signal_lengths","variables",".","Args",":","probs","(","torch.tensor",")","Predicted","probability","value","which","ranges","from","0","to","1",".","Sigmoid","output","is","expected",".","labels","(","torch.tensor",")","Groundtruth","label","for","the","predicted","samples",".","signal_lengths","(","torch.tensor",")",":","The","actual","length","of","the","sequence","without","zero-padding",".","Returns",":","loss","(","NeuralType",")","Binary","cross","entropy","loss","value","."]
code: def forward(self, probs, labels, signal_lengths):
      """
      Calculate binary cross entropy loss based on probs, labels and signal_lengths variables.
  
      Args:
          probs (torch.tensor)
              Predicted probability value which ranges from 0 to 1. Sigmoid output is expected.
          labels (torch.tensor)
              Groundtruth label for the predicted samples.
          signal_lengths (torch.tensor):
              The actual length of the sequence without zero-padding.
  
      Returns:
          loss (NeuralType)
              Binary cross entropy loss value.
      """
      probs_list = [
          probs[k, : signal_lengths[k], :]
          for k in range(probs.shape[0])
      ]
      targets_list = [
          labels[k, : signal_lengths[k], :]
          for k in range(labels.shape[0])
      ]
      probs = torch.cat(probs_list, dim=0)
      labels = torch.cat(targets_list, dim=0)
      return self.loss_f(probs, labels)
  
file_path: NeMo/nemo/collections/asr/losses/bce_loss.py
imports: import torch
  from nemo.core.classes import Loss, Typing, typecheck
  from nemo.core.neural_types import LabelsType, LengthsType, LossType, NeuralType, ProbsType
label_desc: Use image node_id 4 for calling the BCELoss obj's underlying member method code with example usage: obj.forward(probs, labels, signal_lengths) and returns: self